Integrate Navitia with Claude, Cursor, Chatbots & AI Agents MCP Server
GDPR Free for SubscribersCompatible with every major AI agent and IDE
GeminiGet arrivals on Navitia
Returns list of arriving services with line names and codes, origins, scheduled and real-time arrival times, platform information, delay indicators, and mode types. Essential for passenger pickup coordination, arrival monitoring, connection planning, and real-time arrival boards. AI agents use this when users ask "when does the next train arrive at this station", "show incoming services at stop X", or need to track arriving services for passenger coordination. Supports both theoretical schedules and real-time arrival predictions when operator data feeds are available. Get upcoming arrivals at a specific transit stop
Get coverage on Navitia
Shows which cities and metropolitan areas are covered, data freshness indicators, and the contributing transit authorities for each region. Essential for discovering which transit networks are accessible through the API, validating region IDs for subsequent queries, understanding data coverage scope, and planning integration scope. AI agents should use this when users ask "what cities does Navitia cover", "show me all available transit regions", or need to identify the correct region ID (e.g., "fr-idf" for Paris/Ile-de-France) before making region-specific queries for lines, disruptions, or journeys. List all available coverage regions in the Navitia platform
Get departures on Navitia
Returns list of departing services with line names and codes, destinations, scheduled and real-time departure times, platform or bay information, delay indicators, direction codes, and physical/commercial mode types (metro, bus, tram, RER, Transilien). Supports real-time data when available from operators. Essential for passenger information displays, departure boards, real-time transit monitoring, and journey planning. AI agents should reference this when users ask "when is the next metro from this station", "show departures from stop ID X", or need to monitor upcoming services at a known transit stop. Use data_freshness parameter to choose base_schedule (theoretical timetable) or realtime (including disruptions and delays). Get upcoming departures from a specific transit stop
Get disruptions on Navitia
Returns active disruptions with affected lines, routes, stops, and networks, disruption descriptions, severity levels (minor, major, blocking), start and end timestamps, cause types (incident, maintenance, strike, weather), impact descriptions, and detour or alternative service recommendations. Covers all modes including metro, bus, tram, RER, Transilien, and regional rail across French and European networks. Essential for disruption awareness, passenger communication, journey reliability monitoring, and travel planning during service changes. AI agents should reference this when users ask "are there any disruptions on the Paris metro", "is there a strike on SNCF trains", or need to check service reliability before planning journeys. Get active service disruptions and alerts for a transit region
Get isochrone on Navitia
Returns GeoJSON polygon boundaries, reachable area statistics, travel time bands, and accessibility metrics. Essential for urban planning, real estate location analysis, accessibility studies, job market research, school catchment analysis, and understanding transit connectivity. AI agents use this when users ask "what area can I reach within 30 minutes by metro from this address", "show me the accessible zone in 45 minutes by public transport", or need to analyze geographic accessibility from a specific location for housing, employment, or service planning. Generate an isochrone map showing reachable area from a point within a time limit
Get lines on Navitia
Returns lines with codes, names, network affiliations, physical modes (metro, bus, tram, RER, rail), commercial modes, colors, text colors, route counts, and operational information. Covers metro systems (RATP Paris, TCL Lyon, TCL Marseille), bus networks, tramway systems, RER lines, Transilien suburban rail, and regional TER services across France. Essential for transit network exploration, line identification, route planning context, network analysis, and understanding service coverage by mode type. AI agents should use this when users ask "list all metro lines in Paris", "show me all tram lines in Lyon", or need line metadata to understand transit network structure and operator affiliations. List all transit lines in a coverage region
Get nearby stops on Navitia
Returns nearby objects sorted by distance with coordinates, names, types (stop point, stop area, station, address, POI), distances from search point, served lines, and administrative information. Essential for location-based transit discovery, "stops near me" features, geographic transit analysis, multimodal connection identification, and traveler navigation. AI agents use this when users ask "what metro stations are near my current location", "find transit stops within 500m of these coordinates", or need to discover accessible transit options from a specific geographic point. Supports filtering by object type (stop_point, stop_area, poi, address) and adjustable search radius. Find transit stops near a geographic coordinate
Get networks on Navitia
Returns network information including names, codes, contributing authorities, coverage areas, associated lines and routes, and operational status. Covers major operators like RATP (Paris metro/bus/tram), SNCF (RER/Transilien/TER), TCL (Lyon), RTM (Marseille), TCL (Toulouse), and dozens of regional and local operators across France. Essential for operator research, network scoping, regional transit analysis, and understanding service governance structure. AI agents should reference this when users ask "what operators run transit in Paris", "list all networks in Ile-de-France", or need to identify transit operators for a specific region before querying lines or disruptions. List all transit operators and networks in a coverage region
Get stop schedule on Navitia
Returns all scheduled departures with routes, destinations, first and last departure times, service frequency, headway signatures (days of operation), and physical/commercial mode information. Shows complete timetable structure including weekday, weekend, and holiday service patterns. Essential for comprehensive schedule analysis, journey planning at specific times, timetable visualization, and understanding service frequency throughout the day. AI agents should use this when users ask "show me the full timetable for this metro station", "what times does this bus run on Sundays", or need complete schedule data for a transit stop. Supports depth parameter to control level of detail in route and destination information. Get full timetable for a specific transit stop
Plan journey on Navitia
Supports combining public transit (metro, bus, tram, regional trains, high-speed rail), walking, cycling, car, bike-sharing (Vélib), and ridesharing. Returns complete itineraries with departure and arrival times, total duration, number of transfers, detailed legs with mode types, line names, operators, intermediate stops, walking distances, real-time disruption alerts, accessibility information (wheelchair access), and fare estimates. Essential for travel planning, multimodal route comparison, passenger information systems, and Mobility-as-a-Service applications across France and European cities. AI agents should use this when users ask "how do I get from Gare du Nord to Eiffel Tower", "plan a trip from Lyon Part-Dieu to Marseille", or need multimodal journey options with timing, transfers, and accessibility details. Supports traveler profiles including wheelchair, slow walker, fast walker, and luggage. Plan a multimodal trip between two locations in France or Europe
Search places on Navitia
Returns transit stops (stop areas, stop points), stations (metro, tram, bus, rail), addresses, administrative areas, and points of interest with their IDs, names, coordinates, types, and administrative information. Supports autocomplete-style search for journey planning interfaces and location discovery. Essential for stop discovery, address resolution, geocoding, journey origin/destination identification, and building location-based transit features. AI agents should use this when users ask "find the metro station near Champs-Elysees", "search for stops called Republique", or need to identify place IDs and coordinates for use in journey planning queries. Results include embedded links to departures, schedules, and nearby objects for further exploration. Search for transit stops, stations, addresses, and POIs by name
Security & Code Integrity Audit
Every tool in the Navitia MCP Server is continuously audited by the Vinkius Security Engine. We guarantee zero-trust payload isolation, strict data boundaries, and deterministic execution for enterprise-grade AI agents.
How Vinkius protects your data
Is there a risk of the AI "going crazy" and deleting important company data?
No. With Vinkius, the AI operates on "rails". It can only make the exact moves you authorized in the tool's settings. It cannot invent routes, access other networks in your company, or decide to delete random files. If the action isn't in the approved catalog, the attempt is blocked instantly.
Can I audit what my AI agents are doing with this integration?
Yes, Vinkius provides an immutable, HMAC-chained audit log. Every tool execution, payload, and response is tracked in real-time on your dashboard, giving you complete visibility into your agent's actions.
How does the AI access my passwords and credentials?
It simply doesn't. On Vinkius, your passwords, API keys, and login details are kept in a secure vault. The AI (like ChatGPT or Claude) merely "asks" Vinkius to perform the task. Vinkius opens the door, does the work, and hands the result back to the AI. Your credentials are never seen, read, or learned by the artificial intelligence.
How do I check if there are any metro or bus disruptions affecting my planned route in Paris?
Use the get_disruptions tool with the region parameter set to "fr-idf" for Ile-de-France (Paris region). This returns all active disruptions with affected lines, routes, stops, severity levels, cause types (incident, maintenance, strike, weather), start and end timestamps, and impact descriptions. You can also check disruptions directly within journey planning results, as Navitia automatically injects disruption information into journey responses. For station-specific checks, use get_departures which includes delay and cancellation indicators for individual services.
What can AI Agents do with Navitia?
We map standard API endpoints to agent-compatible instructions. Connect Navitia to execute these core functional operations.
Next-Gen journey planning Operations
The Navitia toolkit provides secure access to journey planning functions. It enables conversational agents to manage transportation logistics settings deterministically.
Cursor Copilot for real time transit
Use the Navitia server to execute real time transit operations from your AI agent. The protocol manages state and authentication for continuous transportation logistics workflows.
Navitia. Runs on everything.
From IDE to framework. Every connection governed by Vinkius.
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
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